Target representation and classification using random graphs

Firooz Sadjadi

doi:10.1117/12.2239902

12 May 2016 Target representation and classification using random graphs

Firooz Sadjadi

Proceedings Volume 9844, Automatic Target Recognition XXVI; 98440I (2016) https://doi.org/10.1117/12.2239902
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

In this paper a novel method is described for representation and classification of target by random graphs. A target is represented in terms of set primitives that jointly represent a random graph structure. Random graph is a graph structure with randomly varying vertex and arc attribute values. Random graphs and their statistical and matrix representations are useful when one encounters the problem of classifying signatures of partially occluded targets. We present a number of observations that spectra of random graphs of partially occluded and non-occluded target signatures are related through an interlacing rule and the correlations of their Laplacians lead to robust classification.

Citation Download Citation

Firooz Sadjadi "Target representation and classification using random graphs", Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440I (12 May 2016); https://doi.org/10.1117/12.2239902

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available